Manshaei Roozbeh, Sobhe Bidari Pooya, Aliyari Shoorehdeli Mahdi, Feizi Amir, Lohrasebi Tahmineh, Malboobi Mohammad Ali, Kyan Matthew, Alirezaie Javad
Electrical and Computer Engineering Department, Ryerson University, Toronto, ON, Canada M5B 2K3.
Electrical and Computer Engineering Department, K.N. Toosi University of Technology, Tehran 16315-1355, Iran.
ISRN Bioinform. 2012 Nov 1;2012:419419. doi: 10.5402/2012/419419. eCollection 2012.
Reverse engineering of gene regulatory networks (GRNs) is the process of estimating genetic interactions of a cellular system from gene expression data. In this paper, we propose a novel hybrid systematic algorithm based on neurofuzzy network for reconstructing GRNs from observational gene expression data when only a medium-small number of measurements are available. The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. The algorithm uses neurofuzzy network to model genes effects on other genes followed by four stages of decision making to extract gene interactions. One of the main features of the proposed algorithm is that an optimal number of fuzzy rules can be easily and rapidly extracted without overparameterizing. Data analysis and simulation are conducted on microarray expression profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO. The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task.
基因调控网络（GRNs）的逆向工程是从基因表达数据估计细胞系统遗传相互作用的过程。在本文中，我们提出了一种基于神经模糊网络的新型混合系统算法，用于在仅有中少量测量数据可用时，从观测到的基因表达数据重建基因调控网络。该方法使用模糊逻辑将基因表达值转换为定性描述符，这些描述符可通过一组定义的规则进行评估。该算法使用神经模糊网络对基因对其他基因的影响进行建模，随后通过四个决策阶段来提取基因相互作用。所提出算法的主要特点之一是可以轻松快速地提取最优数量的模糊规则，而不会出现过参数化问题。对酿酒酵母细胞周期的微阵列表达谱进行了数据分析和模拟，结果表明，与四种已发表的算法（深度信念网络（DBNs）、变分贝叶斯期望最大化（VBEM）、时延排列组合因果网络推断（time delay ARACNE）和套索惩罚的概率流形（PF subjected to LASSO））相比，所提出的算法不仅能准确选择时间序列基因表达数据的模式，还能提供具有更高重建精度的模型。针对网络重建任务，从召回率和F值方面评估了所提出方法的准确性。
